1 research outputs found
HEAX: An Architecture for Computing on Encrypted Data
With the rapid increase in cloud computing, concerns surrounding data
privacy, security, and confidentiality also have been increased significantly.
Not only cloud providers are susceptible to internal and external hacks, but
also in some scenarios, data owners cannot outsource the computation due to
privacy laws such as GDPR, HIPAA, or CCPA. Fully Homomorphic Encryption (FHE)
is a groundbreaking invention in cryptography that, unlike traditional
cryptosystems, enables computation on encrypted data without ever decrypting
it. However, the most critical obstacle in deploying FHE at large-scale is the
enormous computation overhead.
In this paper, we present HEAX, a novel hardware architecture for FHE that
achieves unprecedented performance improvement. HEAX leverages multiple levels
of parallelism, ranging from ciphertext-level to fine-grained modular
arithmetic level. Our first contribution is a new highly-parallelizable
architecture for number-theoretic transform (NTT) which can be of independent
interest as NTT is frequently used in many lattice-based cryptography systems.
Building on top of NTT engine, we design a novel architecture for computation
on homomorphically encrypted data. We also introduce several techniques to
enable an end-to-end, fully pipelined design as well as reducing on-chip memory
consumption. Our implementation on reconfigurable hardware demonstrates
164-268x performance improvement for a wide range of FHE parameters.Comment: To appear in proceedings of ACM ASPLOS 202